Device and method for dynamically reducing interference

ABSTRACT

A device is configured to calculate a set of channel estimates for a plurality of sub-carriers based on a plurality of pilot-symbols and/or data-symbols carried by said plurality of sub-carriers; calculate a set of equalizers for the plurality of sub-carriers based on the set of channel estimates; perform an equalization on a plurality of data-symbols using the set of equalizers for obtaining a plurality of equalized symbols; perform a soft-slicing or hard-slicing procedure comprising obtaining a plurality of estimated symbols based on the equalized symbols; calculate at least one diagonal-loaded interference-plus-noise covariance matrix, based on the set of estimated symbols; de-map the plurality of data symbols to soft-bits, based on the at least one diagonal-loaded interference-plus-noise covariance matrix, the set of channel estimates or an updated version thereof; and feed the soft-bits to a channel decoder.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/EP2019/078114, filed on Oct. 16, 2019, the disclosure of which ishereby incorporated by reference in its entirety.

TECHNICAL FIELD

This disclosure relates generally to the field of wireless local areanetworks, and particularly, to dynamic interference cancellation inwireless local area networks. To this end, this disclosure provides adevice for a multicarrier communication receiver, and a correspondingmethod, which may detect a drop in post-processing Signal-to-Noise-Ratio(pp-SNR) level, and may enable Dynamic Interference Rejection Combining(D-IRC).

BACKGROUND

Generally, the performance of a wireless local area network (forexample, WiFi performance) is often interference limited as opposed tonoise.

For example, there may be several scenarios of interference in WiFi, asfollows:

-   -   Interference can be WiFi or Non-WiFi.    -   Interference can be static interference or dynamic interference.

FIG. 9 schematically illustrates a diagram 900 of static interference.In the static interference (as it can be derived from diagram 900), theinterferer(s) power (statistics) remains fixed during the whole desiredpacket (including the preamble).

FIG. 10 schematically illustrates a diagram 1000 of dynamicinterferences, including Rising 1001, Falling 1002, Rising+falling 1003,and Falling+rising 1004 interferences.

In the dynamic interference (as it can be derived from diagram 1000),the interferer(s) power (statistics) changes within the duration of thedesired packet.

Moreover, even a relatively weak rising interferer can cause seriousdegradation, if the required Signal-to-Noise-ratio (SNR) for the givenMulti-Carrier communication System (MCS) is not met. This is depicted inFIG. 11 , which is a schematic view of a diagram 1100 illustratingMaximum Ratio Combining (MRC) and Interference Rejection Combining (IRC)performance in the case of no interference, static interference, andrising interference.

The WiFi interference is, in general, not synchronized to the desiredpacket, for example, it may rise or fall anywhere within the desiredpacket. Moreover, the WiFi interferer nature may change within thepacket due to, for example, transitions in portions of preamble,transition from single stream to multi stream, etc. Therefore, the purestatic case may be a case when these transitions do not occur within thedesired packet (i.e., rather a rare case).

Furthermore, the dynamic interference is very problematic for the WiFi,since in the WiFi (except for very few phase tracking pilots intendedfor Carrier Frequency Offset (CFO)/phase noise compensation), there areno pilots after the preamble. This means that it may be difficult todetect and mitigate an interference that rises after the preamble.

Perhaps the most important case of dynamic interference is that ofrising interference. The Rising-interference case is due to Hidden-node.

Reference is made to FIG. 12 which is a schematic view of a diagram 1200illustrating the Hidden-node problem.

In the diagram 1200, B 1201 is the Access Point (AP), A 1202 is aStation (STA) that is not sensing C 1203 and transmitting to the B 1201.Moreover, C 1203 is STA that is not sensing the A 1202 and transmittingto B 1201 or another AP. Moreover, C 1203 may also be also another APtransmitting to one of its STAs.

Besides, some other important scenarios are combinations of rising andfalling interferences such as the Rising+falling interference case. Thisscenario may occur due to Hidden-node, where the Hidden-node packet endsbefore the desired packet ends. Also, the dynamic interferencemitigation is essential to handle the rising interference. In addition,another scenario is the Falling+rising interference scenario. Forexample, it may happen due to Message in Message (MIM) followed byacknowledgment (ACK) (for example, message 1 (MSG1) packet ends beforemessage 2 (MSG2)), however, there are other scenarios as well. In thelatter case, rising interferer is different than falling interferer andthe dynamic interference mitigation is essential.

Furthermore, some of the other cases in which dynamic interferencemitigation may help are as follows:

-   -   Combinations of more than one simultaneous interferers (possibly        rising and/or falling), for example, nested Hidden-nodes.    -   Interference that changes within the preamble or static/falling        interference in which the nature of interference changes within        the packet.

For instance, if the interferences described in the different scenariosdiscussed above is not mitigated, it may cause the WiFi to possibly workin a much lower MCS than in the case of no interference, thus reducingconsiderably the throughput. So, even if the AP and the STA beinglocated very close, the data rate may be rather low.

The problem of dynamic interference in WiFi (such as risinginterference) has not been solved explicitly to date in the sense thatthere is no solution for mitigating the dynamic interference. This meansthat in practice the rate adaptation algorithm (e.g., Minstrel) wouldconverge to an MCS that is low enough to support the interference level.This means however that the throughput is significantly decreased due tothe dynamic interference.

Furthermore, unlike the problem of dynamic interference, the problem ofstatic interference has a known solution using the IRC.

Moreover, in a hypothetical system (for example, the system 1300 of FIG.13 ) a general model of y=hs+v may be considered, where v is theinterference+noise with the Covariance of C, h is the channel vector,and s is the transmitted preamble. If assuming a perfect channelknowledge and interference statistics knowledge, the optimal detection(for Gaussian interference) is the Minimum Variance DistortionlessResponse (MVDR), in which:

$\begin{matrix}{{\overset{\hat{}}{s} = \frac{h^{H}C^{- 1}y}{h^{H}C^{- 1}h}}.} & {{Eq}.\mspace{11mu}(1)}\end{matrix}$by using the matrix inversion lemma, it may be possible to replace C⁻¹with R⁻¹, where:R=C+hh ^(H) =E(yy ^(H)).  Eq. (2)This shows that, it may be possible to work with the total Covariance Rinstead of C.

Furthermore, by relaxing the assumption of perfect channel andinterference statistic knowledge, there are two main approaches asfollows:

1. The IRC based on R (Scaled Minimum Mean Square Error (MMSE): that maybe described by Eq. (3), Eq. (4), and Eq. (5), as follows:

$\begin{matrix}{{\overset{\hat{}}{s} = {w^{T}y}},} & {{Eq}.\mspace{11mu}(3)} \\{{w^{T} = \frac{{\hat{h}}^{H}{\hat{R}}^{- 1}}{{\hat{h}}^{H}{\hat{R}}^{- 1}\hat{h}}},} & {{Eq}.\mspace{11mu}(4)} \\{{\hat{R} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{y_{n}y_{n}^{H}}}}},} & {{Eq}.\mspace{11mu}(5)}\end{matrix}$where ĥ is the channel estimate ({circumflex over (R)} is calculatedover a band of N sub-carriers).2. The IRC based on C (Whitening/Minimum Variance DistortionlessResponse (MVDR)), that may be described by Eq. (6), Eq. (7), and Eq.(8), as follows:

$\begin{matrix}{{\hat{s} = {{\frac{{\overset{\sim}{h}}^{H}}{{\overset{\sim}{h}}^{2}}\overset{\sim}{y}} = \frac{{\hat{h}}^{H}{\hat{C}}^{- 1}y}{{\hat{h}}^{H}{\hat{C}}^{- 1}\hat{h}}}},{where},} & {{Eq}.\mspace{11mu}(6)} \\{{\overset{\sim}{y} = {{\hat{C}}^{{- 1}/2}y}},{\overset{\sim}{h} = {{\hat{C}}^{{- 1}/2}\hat{h}}},{and}} & {{Eq}.\mspace{11mu}(7)} \\{\hat{C} = {\frac{1}{N}{\sum{\left( {y_{n} - {h_{n}s_{n}}} \right)\left( {y_{n} - {h_{n}s_{n}}} \right)^{H}}}}} & {{Eq}.\mspace{11mu}(8)}\end{matrix}$is the estimate for the Covariance C (e.g., Ĉ may be calculated over aband of N sub-carriers).

These two approaches are in general not identical for practical channelestimation (e.g., smoothing used in Wi-Fi or Long Term Evolution (LTE)).For example, the IRC is used extensively in LTE where the interferencenature is more static.

Moreover, while the preamble based IRC performs well in the staticinterference case, in general, it does not null a rising interferer. Forinstance, the IRC fails if the interferer rises or changes afterpreamble because the beamformer calculated at the preamble isunsuitable. In the case of Wi-Fi the preamble is in the beginning of thepacket and there are no sufficient pilots afterwards so IRC can helponly in limited number of cases. Thus IRC is not an adequate solutionfor Wi-Fi. This may also be derived from diagram 1100 illustrated inFIG. 11 , which shows that (preamble based) IRC works very well when theinterferer is static, whereas there is total loss when (preamble based)IRC is used for rising interferer.

SUMMARY

In view of the above-mentioned problems and disadvantages, embodimentsof this disclosure aim to improve the conventional devices and methods.An objective is to solve the technical problem of dynamic interferencein WiFi, by mitigating the interferer, thus increasing thepost-processing SINR and as a consequence increasing throughput.

The objective is achieved by the embodiments provided in the enclosedindependent claims. Advantageous implementations of the embodiments arefurther defined in the dependent claims.

In particular the embodiments of this disclosure propose estimating thecovariance like in IRC and (only that) the unknown symbols s_(n) arereplaced by s _(n), which is the soft-slicing symbols according to

$\begin{matrix}{{\hat{C} = {\frac{1}{N}{\sum{\left( {y_{n} - {{\hat{h}}_{n}{\overset{\_}{s}}_{n}}} \right)\left( {y_{n} - {{\hat{h}}_{n}{\overset{\_}{s}}_{n}}} \right)^{H}}}}},} & {{Eq}.\mspace{11mu}(9)}\end{matrix}$wheres _(n) =s _(mid_r,n)+tan h(2γ_(n)(Re(ŝ _(n))−s _(mid_r,n)))+j(s_(mid_i,n)+tan h(2γ_(n)(Im(ŝ _(n))−s _(mid_i,n)))),ŝ _(n) =w ^(T) y _(n),s_(mid_r,n)=middle point between the PAM constellation points closest toRe(ŝ_(n))s_(mid_i,n)=middle point between the PAM constellation points closest toIm(ŝ_(n))γ_(n)=post processing SNR

Moreover, an iterative algorithm may be used to refine the symbols s_(n) and as a consequence refine the covariance and beamformer.Furthermore, a post-processing SNR detector may be used to detect therising/dynamic interference and enable D-IRC only when it is necessary.This may enable having no degradation in the case of no interference orstatic interference and in the meantime may have (huge) gains in thecase of dynamic interference.

The main advantages of the embodiments of this disclosure can besummarized as follows:

-   -   Achieving a significant performance gain in the presence of        dynamic interference.    -   No negative gain (no degradation when there is no interferer or        there is static interferer).    -   Feasible computational complexity.

A first aspect of this disclosure provides a device for dynamicallyreducing interference for a multi-carrier communication receiver,wherein the device is configured to calculate a set of channel estimatesfor a plurality of sub-carriers based on a plurality of pilot-symbolsand/or data-symbols carried by said plurality of sub-carriers; calculatea set of equalizers for the plurality of sub-carriers based on the setof channel estimates; perform an equalization on a plurality ofdata-symbols using the set of equalizers for obtaining a plurality ofequalized symbols; perform a soft-slicing or hard-slicing procedurecomprising obtaining a plurality of estimated symbols based on theequalized symbols; calculate at least one diagonal-loadedinterference-plus-noise covariance matrix, based on the set of estimatedsymbols; de-map the plurality of data symbols to soft-bits, based on theat least one diagonal-loaded interference-plus-noise covariance matrix,the set of channel estimates or an updated version thereof; and feed thesoft-bits to a channel decoder.

The device, may be or may incorporated in, a multi-carrier communicationreceiver. The device may comprise a circuitry. The circuitry maycomprise hardware and software. The hardware may comprise analog ordigital circuitry, or both analog and digital circuitry. In someembodiments, the circuitry comprises one or more processors and anon-volatile memory connected to the one or more processors. Thenon-volatile memory may carry executable program code which, whenexecuted by the one or more processors, causes the device to perform theoperations or methods described herein.

For example, in some embodiments, the device may be supplied with adynamic interference mitigation scheme which may use a detector based onpost-processing SNR to decide whether to use (the conventional) MRCmethod or IRC method or to switch to the dynamic IRC (D-IRC), forexample, once an SNR drop is detected the switch is done. The device mayuse the D-IRC. The D-IRC principles are based on IRC. IRC in the staticinterference case is straightforward since it can be done on thepreamble. In the dynamic interference case, the difficulty comes fromthe fact that the interference nature changes after the preamble so itcannot be used (no pilots within the payload in Wi-Fi).

For example, in some embodiments, the device may use the received datasymbols to estimate the covariance of the interference and may furtherapply IRC/whitening. The difficulty in this approach may come from thefact that the data symbols are unknown to the receiver. In someembodiments, one possibility is to use hard-decisions, but in someembodiments a more general approach may be using soft-slicing (e.g.,based on conditional mean estimator) instead of hard-decisions toestimate the covariance (e.g., the hard decision can be seen as aspecial case of soft-slicing).

In some embodiments, (a difficulty may be the fact that) soft-slicingmay require the knowledge of beamformer/equalizer (which requires thesoft-slicing) and soft-slicing also requires knowledge ofpost-processing SNR, which is also unknown before beamformer/equalizeris calculated. To overcome this (i.e., “chicken and egg”) problem, thedevice may perform following steps:

-   -   1. The device may obtain the initial beamformer/equalizer (w₀)        and post-processing SNR (ppSNR₀) from previous symbol.    -   2. For i=1:Number of Iterations, the device may        -   a. perform soft-slicing,        -   b. estimate the covariance and recalculate the            beamformer/equalizer−w_(i),        -   c. recalculate the post-processing SNR−ppSNR_(i).    -   3. The device may use the last calculated beamformer/equalizer        for equalization.    -   4. The device may use the last calculated post-processing SNR        for Log Likelihood ratio (LLR) scaling.

The provided D-IRC scheme is not limited to the case of a single streamand can be used in Single User Multiple-Input and Multiple-Output(SU-MIMO) and Multi User (MU-MIMO).

In an implementation form of the first aspect, the device is furtherconfigured to calculate a subsequent set of equalizers for the pluralityof sub-carriers, based on the at least one diagonal-loadedinterference-plus-noise covariance matrix and the set of channelestimates or an updated version thereof.

In a further implementation form of the first aspect, the device isfurther configured to calculate at least one whitening matrix based onthe calculated at least one diagonal-loaded interference-plus-noisecovariance matrix; calculate the subsequent set of equalizers for theplurality of sub-carriers based on the at least one whitening matrix;and de-map the plurality of data symbols into soft-bits, based on the atleast one whitening matrix.

In a further implementation form of the first aspect, the device isfurther configured to perform a whitening operation on the plurality ofdata-symbols, using the calculated at least one whitening matrix, forobtaining a plurality of whitened data symbols; perform a whiteningoperation on either the set of channel estimates or an updated versionthereof, using the calculated at least one whitening matrix, forobtaining a set of whitened channel estimates; calculate the subsequentset of equalizers for the plurality of sub-carriers based on the set ofwhitened channel estimates; and obtain the soft-bits by performing ade-mapping operation on the whitened data symbols, based on thesubsequent set of equalizers and/or the whitened channel estimates.

In some embodiments, the calculation of the pp-SNR may not be necessary,e.g., if the soft-slicing is replaced by hard decision, for example, thepp-SNR is not critical for performance.

In some embodiments, in Maximum likelihood decoding (MLD) decoder, thesubsequent set of equalizers is used only on the next symbol; onlywhitening is done on the plurality of data-symbols. In some embodiments,in the case of linear receiver, whitening can be absorbed into theequalizer. In some embodiments, the whitening may not be done implicitlybut may be performed explicitly through the equalization.

In some embodiments, in both the single stream and the multi streamcase, the whitening+equalization can be done in several ways (all aremathematically equivalent), as follows:

-   -   1. The input vector may be whitened and the channel estimates        may be whitened. Then equalizer may be calculated based on        whitened channel estimates.    -   2. Whitening can be done explicitly by absorbing the whitening        operation of both the input and channel estimates into the        equalizer.    -   3. A combination of 1 and 2, e.g., whitening may be performed on        the input and the channel estimates. Whitening may be absorbed        into the equalizer or the input whitening may be absorbed into        the equalizer and the equalizer may be based on whitened channel        estimates.

In a further implementation form of the first aspect, the device isfurther configured to calculate at least one interference-plus-noisecovariance matrix and add a diagonal matrix to the at least oneinterference-plus-noise covariance matrix for calculating the at leastone diagonal-loaded interference-plus-noise covariance matrix.

In a further implementation form of the first aspect, the device isfurther configured to calculate an inverse Cholesky matrix of the atleast one diagonal-loaded interference-plus-noise covariance matrix.

For example, the device may calculate the inverse Cholesky matrix of theat least one diagonal-loaded interference-plus-noise covariance matrixbased on obtaining a triangular matrix by performing a Choleskydecomposition on the at least one diagonal-loadedinterference-plus-noise covariance matrix; and inverting the obtainedtriangular matrix.

In a further implementation form of the first aspect, the inverseCholesky matrix is calculated by performing a Cholesky decomposition onthe at least one diagonal-loaded interference-plus-noise covariancematrix.

In a further implementation form of the first aspect, performing thewhitening and equalization comprises whitening a received input vectorfor a determined subcarrier, SC, based on the inverse Cholesky matrixand subsequently applying the subsequent set of equalizers on thewhitened input vector for the determined SC; or applying the subsequentset of equalizers on the inverse Cholesky matrix and subsequentlyapplying the obtained result on a received input vector for a determinedsubcarrier.

In a further implementation form of the first aspect, the device isfurther configured to perform an inner-iteration procedure comprisingperforming a predefined number of inner-iterations, wherein eachinner-iteration is performed on the same plurality of data-symbols, andwherein in each inner-iteration, the following steps are performed:

performing the equalization using the set of equalizers calculated inthe previous inner-iteration or in the case of first inner-iterationusing the set of equalizers calculated for the plurality of sub-carriersbased on the set of channel estimates,

performing the soft-slicing procedure or the hard-slicing procedure,

calculating the at least one diagonal-loaded interference-plus-noisecovariance matrix,

calculating the at least one whitening matrix,

performing the whitening operation on the plurality of data-symbols,

performing the whitening operation for obtaining the set of whitenedchannel estimates, and

calculating another set of equalizers.

In a further implementation form of the first aspect, the device isfurther configured to perform an outer-iteration procedure comprisingperforming a predefined number of outer-iterations, wherein eachouter-iteration is performed on a different plurality of data-symbolsthan the previous outer-iteration, and wherein in each outer-iterationthe following steps are performed:

performing the equalization using the set of equalizers calculated inthe previous outer-iteration or an inner-iteration or in the case offirst outer-iteration using the set of equalizers calculated for theplurality of sub-carriers based on the set of channel estimates,

performing the soft-slicing procedure or the hard-slicing procedure,

calculating the at least one diagonal-loaded interference-plus-noisecovariance matrix, calculating the at least one whitening matrix,

performing the whitening operation on the plurality of data-symbols,

performing the whitening operation for obtaining the set of whitenedchannel estimates, and

calculating another set of equalizers.

In a further implementation form of the first aspect, the whitenedchannel estimates are obtained by multiplying at least one channelestimates matrix for a determined SC or the updated version thereof bythe calculated at least one whitening matrix, in particular the inverseCholesky matrix.

In a further implementation form of the first aspect, the device isfurther configured to divide a plurality of SCs into sub-bands, whereineach sub-band comprises a predetermined number of SCs, and wherein aninterference-plus-noise covariance matrix is estimated per eachsub-band.

In a further implementation form of the first aspect, the device isfurther configured to calculate a first set of per-stream postprocessing Signal-to-Interference-Plus-Noise Ratio (pp-SINR) for aplurality of sub-carriers based on a plurality of pilot-symbol and/ordata-symbols and/or the set of channel estimates carried by saidplurality of sub-carriers; and calculate a second set of per streampp-SINR, for the plurality of sub-carriers based on a plurality ofequalized symbols and a plurality of estimated symbols.

In a further implementation form of the first aspect, the device isfurther configured to calculate from the first set of per-stream pp-SINRa single per-stream pp-SINR vector representing an average pp-SINR ofthe first set; calculate from the second set of per-stream pp-SINR asingle per-stream pp-SINR vector representing an average pp-SINR of thesecond set; calculate pp-SINR difference vector as the differencebetween the average pp-SINR of the first set and the average pp-SINR ofthe second set.

In a further implementation form of the first aspect, the device isfurther configured to detect a drop in pp-SINR level, for the pluralityof SCs.

In a further implementation form of the first aspect, the drop in thepp-SINR is detected based on the pp-SINR difference vector.

In a further implementation form of the first aspect, the device isenabled, in particular, wherein the predefined number ofinner-iterations and/or outer-iterations is determined, based on adetected drop in pp-SINR level.

In a further implementation form of the first aspect, the set ofequalizers and/or the subsequent set of equalizers is based on a MinimumMean Square Error (MMSE) equalizer or a Zero Forcing (ZF) equalizer.

A second aspect of this disclosure provides a method for dynamicallyreducing interference for a multi-carrier communication receiver,wherein the method comprises calculating a set of channel estimates fora plurality of sub-carriers based on a plurality of pilot-symbols and/ordata-symbols carried by said plurality of sub-carriers; calculating aset of equalizers for the plurality of sub-carriers based on the set ofchannel estimates; performing an equalization on a plurality ofdata-symbols using the first set of equalizers for obtaining a pluralityof equalized symbols; performing a soft-slicing or hard-slicingprocedure comprising obtaining a plurality of estimated symbols based onthe equalized symbols; calculating at least one diagonal-loadedinterference-plus-noise covariance matrix, based on the set of estimatedsymbols; de-mapping the plurality of data symbols to soft-bits, based onthe at least one diagonal-loaded interference-plus-noise covariancematrix, the first set of channel estimates or an updated versionthereof; and feeding the soft-bits to a channel decoder.

In an implementation form of the second aspect, the method furthercomprises calculating a subsequent set of equalizers for the pluralityof sub-carriers, based on the at least one diagonal-loadedinterference-plus-noise covariance matrix and the set of channelestimates or an updated version thereof.

In a further implementation form of the second aspect the method furthercomprises calculating at least one whitening matrix based on thecalculated at least one diagonal-loaded interference-plus-noisecovariance matrix; calculating the subsequent set of equalizers for theplurality of sub-carriers based on the at least one whitening matrix;and de-mapping the plurality of data symbols into soft-bits, based onthe at least one whitening matrix.

In a further implementation form of the second aspect, the methodfurther comprises performing a whitening operation on the plurality ofdata-symbols, using the calculated at least one whitening matrix, forobtaining a plurality of whitened data symbols; performing a whiteningoperation on either the set of channel estimates or an updated versionthereof, using the calculated at least one whitening matrix, forobtaining a set of whitened channel estimates; calculating thesubsequent set of equalizers for the plurality of sub-carriers based onthe set of whitened channel estimates; and obtaining the soft-bits byperforming a de-mapping operation on the whitened data symbols, based onthe subsequent set of equalizers and/or the whitened channel estimates.

In a further implementation form of the second aspect, the methodfurther comprises calculating at least one interference-plus-noisecovariance matrix and add a diagonal matrix to the at least oneinterference-plus-noise covariance matrix for calculating the at leastone diagonal-loaded interference-plus-noise covariance matrix.

In a further implementation form of the second aspect, the methodfurther comprises calculating an inverse Cholesky matrix of the at leastone diagonal-loaded interference-plus-noise covariance matrix.

In a further implementation form of the second aspect, the inverseCholesky matrix is calculated by performing a Cholesky decomposition onthe at least one diagonal-loaded interference-plus-noise covariancematrix.

In a further implementation form of the second aspect, performing thewhitening and equalization comprises whitening a received input vectorfor a determined subcarrier, SC, based on the inverse Cholesky matrixand subsequently applying the subsequent set of equalizers on thewhitened input vector for the determined SC; or applying the subsequentset of equalizers on the inverse Cholesky matrix and subsequentlyapplying the obtained result on a received input vector for a determinedsubcarrier.

In a further implementation form of the second aspect, the methodfurther comprises performing an inner-iteration procedure comprisingperforming a predefined number of inner-iterations, wherein eachinner-iteration is performed on the same plurality of data-symbols, andwherein in each inner-iteration, the following steps are performed:

performing the equalization using the set of equalizers calculated inthe previous inner-iteration or in the case of first inner-iterationusing the set of equalizers calculated for the plurality of sub-carriersbased on the set of channel estimates,

performing the soft-slicing procedure or the hard-slicing procedure,

calculating the at least one diagonal-loaded interference-plus-noisecovariance matrix,

calculating the at least one whitening matrix,

performing the whitening operation on the plurality of data-symbols,

performing the whitening operation for obtaining the set of whitenedchannel estimates, and

calculating another set of equalizers.

In a further implementation form of the second aspect, the methodfurther comprises performing an outer-iteration procedure comprisingperforming a predefined number of outer-iterations, wherein eachouter-iteration is performed on a different plurality of data-symbolsthan the previous outer-iteration, and wherein in each outer-iterationthe following steps are performed:

performing the equalization using the set of equalizers calculated inthe previous outer-iteration or an inner-iteration or in the case offirst outer-iteration using the set of equalizers calculated for theplurality of sub-carriers based on the set of channel estimates,

performing the soft-slicing procedure or the hard-slicing procedure,

calculating the at least one diagonal-loaded interference-plus-noisecovariance matrix, calculating the at least one whitening matrix,

performing the whitening operation on the plurality of data-symbols,

performing the whitening operation for obtaining the set of whitenedchannel estimates, and

calculating another set of equalizers.

In a further implementation form of the second aspect, the whitenedchannel estimates are obtained by multiplying at least one channelestimates matrix for a determined SC or the updated version thereof bythe calculated at least one whitening matrix, in particular the inverseCholesky matrix.

In a further implementation form of the second aspect, the methodfurther comprises dividing a plurality of SCs into sub-bands, whereineach sub-band comprises a predetermined number of SCs, and wherein aninterference-plus-noise covariance matrix is estimated per eachsub-band.

In a further implementation form of the second aspect, the methodfurther comprises calculating a first set of per-stream post processingSignal-to-Interference-Plus-Noise Ratio, pp-SINR, for a plurality ofsub-carriers based on a plurality of pilot-symbol and/or data-symbolsand/or the set of channel estimates carried by said plurality ofsub-carriers; and calculating a second set of per stream pp-SINR, forthe plurality of sub-carriers based on a plurality of equalized symbolsand a plurality of estimated symbols.

In a further implementation form of the second aspect, the methodfurther comprises calculating from the first set of per-stream pp-SINR asingle per-stream pp-SINR vector representing an average pp-SINR of thefirst set; calculating from the second set of per-stream pp-SINR asingle per-stream pp-SINR vector representing an average pp-SINR of thesecond set; calculating pp-SINR difference vector as the differencebetween the average pp-SINR of the first set and the average pp-SINR ofthe second set.

In a further implementation form of the second aspect, the methodfurther comprises detecting a drop in pp-SINR level, for the pluralityof SCs.

In a further implementation form of the second aspect, the drop in thepp-SINR is detected based on the pp-SINR difference vector.

In a further implementation form of the second aspect, the method isenabled, in particular, the predefined number of inner-iterations and/orouter-iterations is determined, based on a detected drop in pp-SINRlevel.

In a further implementation form of the second aspect, the set ofequalizers and/or the subsequent set of equalizers is based on a MinimumMean Square Error, MMSE, equalizer or a Zero Forcing, ZF, equalizer.

A third aspect of this disclosure provides a computer program which,when executed by a computer, causes the method of second aspect or oneof the implementation form of the second aspect to be performed.

In some embodiments, the computer program may be provided on anon-transitory computer-readable recording medium.

It has to be noted that all devices, elements, units and means describedin the present application could be implemented in the software orhardware elements or any kind of combination thereof. All steps whichare performed by the various entities described in the presentapplication as well as the functionalities described to be performed bythe various entities are intended to mean that the respective entity isadapted to or configured to perform the respective steps andfunctionalities.

Even if, in the following description of specific embodiments, aspecific functionality or step to be performed by external entities isnot reflected in the description of a specific detailed element of thatentity which performs that specific step or functionality, it should beclear for a skilled person that these methods and functionalities can beimplemented in respective software or hardware elements, or any kind ofcombination thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

The above described aspects and implementation forms of this disclosurewill be explained in the following description of specific embodimentsin relation to the enclosed drawings, in which

FIG. 1 is a schematic view of a device for dynamically reducinginterference for a multi-carrier communication receiver, according to anembodiment of this disclosure.

FIG. 2 is shows a diagram illustrating results for a scenario of risinginterference, arriving in the ˜middle of a received frame.

FIG. 3 shows of a diagram illustrating a comparison of the performanceof D-IRC to MRC and IRC.

FIG. 4 schematically illustrates a very significant gain in goodput forlow and medium SIR.

FIG. 5 shows a diagram illustrating a comparison of the performance ofD-IRC to that of IRC/MRC in the case that there is no interference.

FIG. 6 shows of a D-IRC block diagram.

FIG. 7 is a schematic view of a D-IRC SNR detector processing until SINRdrop is detected.

FIG. 8 is a flowchart of a method for dynamically reducing interferencefor a multi-carrier communication receiver, according to an embodimentof this disclosure.

FIG. 9 shows a diagram of static interference.

FIG. 10 shows a diagram of dynamic interferences including Rising,Falling, Rising+falling, and Falling+rising interferences.

FIG. 11 shows a diagram illustrating MRC and IRC performance in the caseof no interference, static interference, and rising interference.

FIG. 12 shows a diagram illustrating a Hidden-node problem.

FIG. 13 schematically illustrates a conventional AP receiving WiFipackets from an STA in the presence of interferer.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

FIG. 1 is a schematic view of a device 100 for dynamically reducinginterference for a multi-carrier communication receiver, according to anembodiment of this disclosure.

The device 100 is configured to calculate a set of channel estimates 101for a plurality of sub-carriers no based on a plurality of pilot-symbolsand/or data-symbols carried by said plurality of sub-carriers 110.

The device 100 is further configured to calculate a set of equalizers102 for the plurality of sub-carriers no based on the set of channelestimates 101.

The device 100 is further configured to perform an equalization on aplurality of data-symbols using the set of equalizers 102 for obtaininga plurality of equalized symbols 103.

The device 100 is further configured to perform a soft-slicing orhard-slicing procedure comprising obtaining a plurality of estimatedsymbols 104 based on the equalized symbols 103.

The device 100 is further configured to calculate at least onediagonal-loaded interference-plus-noise covariance matrix 105, based onthe set of estimated symbols 104.

The device 100 is further configured to de-map the plurality of datasymbols to soft-bits 106, based on the at least one diagonal-loadedinterference-plus-noise covariance matrix 105, the set of channelestimates 101 or an updated version thereof.

The device 100 is further configured to feed the soft-bits 106 to achannel decoder.

The device 100 may be a receiver device. The device 100 may comprise acircuitry (not shown in FIG. 1 ). The circuitry may comprise hardwareand software. The hardware may comprise analog or digital circuitry, orboth analog and digital circuitry. In some embodiments, the circuitrycomprises one or more processors and a non-volatile memory connected tothe one or more processors. The non-volatile memory may carry executableprogram code which, when executed by the one or more processors, causesthe device to perform the operations or methods described herein.

Moreover, the device 100 may achieve a significant performance gain inthe presence of dynamic interferer. Reference is made to FIG. 2 which isa schematic view of a diagram 200 illustrating results for scenario ofrising interference, arriving in the ˜middle of a received frame.

In the diagram 200 of FIG. 2 , a comparison of the performance of D-IRCto MRC and IRC is illustrated, in the case of fixed INR of 15 dB for64QAM, rate 5/6. The receiver (RX) uses 8Rx antennas and the channel isTGn-D NLOS. It can be seen that the D-IRC enables working in SNR of ˜18dB (as it is indicated with the reference 202), while it is not possibleto work with the IRC and/or the MRC (indicated with the reference 201indicating a total collapse of standard techniques).

Reference is made to FIG. 3 , which is a schematic view of a diagram 300illustrating a comparison of the performance of D-IRC to MRC and IRC.

In the diagram 300 of FIG. 3 , a comparison of the performance of D-IRC(performed by the device 100) to MRC and IRC is illustrated, in the caseof fixed SNR of 18 dB for 64QAM, rate 5/6. The receiver device (RX) uses8Rx antennas and the channel is TGn-D NLOS. It can be derived that,there is a gain of more than 15 dB. Moreover, the D-IRC (the device 100)enables working with interferer which is even stronger than desiredsignal. The simulation results presented so far showed the D-IRC gain interms of PER for some chosen MCSs. In order to better understand thereal gain of D-IRC in terms of throughput, here, a different approach istaken into account. For example, a determined realistic scenario of anAP with four receiving (RX) antennas receiving a packet from a STA withtwo transmitting (TX) antennas for a certain fixed SNR may be assumed.Moreover, another AP/Station does not sense the STA, and startstransmission somewhere during the desired packet (rising interferer).Furthermore, for each SIR value, it may be determined, over all possibleMCSs with one or two spatial streams and the MCS that maximizes thegoodput may be found.

For example, it may be assumed that the interferer transmits two spatialstreams and polarized antennas are used by the AP, the station, and theinterferer.

Also, this process may be performed, one time with D-IRC being “on” andone time with D-IRC being “off”. Afterwards, the goodput between thesetwo options may be compared. In the diagram 300 of FIG. 3 , a comparisonof the performance of D-IRC being “on” and when the D-IRC being “off”,in the four RX antenna case, in terms of maximum goodput is illustrated.

For example, as it is indicated with the reference 301, the standardtechniques enable working only when the interference is much weaker thanthe desired signal: SIR>˜14 dB.

Moreover, as it is indicated with the reference 302, the D-IRC allowsworking when interference is as strong as the desired signal or evenstronger.

Reference is made to FIG. 4 , which is a schematic view of a diagram 400illustrating maximum goodput per SIR for D-IRC on (one iteration) andD-IRC off with PPM offset and sampling offset.

The diagram 400 of FIG. 4 illustrates a very significant gain in goodputfor low SIR, which decreases as SIR increases, until the gain vanishesfor SIR˜=SNR. The gain of when the D-IRC being “on” 401 relative to whenthe D-IRC being “off” 402 remains significant at moderate SIRs (e.g.,˜50% @SIR10-11 dB).

Moreover, in some embodiments of this disclosure, there is no negativegain (no degradation when there is no interferer or there is staticinterferer).

Reference is made to FIG. 5 , which is a schematic view of a diagram 500illustrating a comparison of the performance of D-IRC 503 to that ofIRC/MRC 501, 502 in the case that there is no interference. It can beseen that there is no degradation relative to MRC.

Moreover, in some embodiments of this disclosure, with respect to thecomputational complexity, the D-IRC usually requires less than threeiterations (even one iteration gives considerable gain) so it isfeasible in terms of complexity.

Reference is made to FIG. 6 , which is a schematic view of a D-IRC blockdiagram 600.

The device 100 may provide (e.g., perform) the D-IRC algorithm, forexample, as it is illustrated in diagram 600 of FIG. 6 . In someembodiments, the D-IRC algorithm may be performed by the device 100.

The D-IRC block diagram 600 may include several variables which arebriefly defined as follows: for a given matrix A, the variable [A]_(i,j)denotes the element in the i^(th) row and j^(th) column of the matrix A.For a given square matrix A, the variable diag(A) denotes the columnvector resulted from taking its diagonal elements.

For a given vector a, the variable [a]_(i) denotes the i^(th) element ofthe vector a. For a given vector a, the variable diag(a) denotes theresulted diagonal matrix such that the [diag(A)]_(i,i)=[a]_(i).

Moreover, 1 denotes a vector of all ones (its dimension is implicitlyimplied from the equation). N_(r) denotes the number of Rx antennas andN_(s) denotes the number of spatial streams. Furthermore, M and d_(min)denote the constellation size and constellation minimum distance,respectively.

In the following, for the sake of illustration, an embodiment of thisdisclosure is discussed for the case that all streams have the sameconstellation. However, the embodiments of this disclosure are notlimited to the above mentioned case, and the results can be extended tothe more general case.

For instance, the SCs are divided into sub-bands, each sub-band has Nsubcarriers. Moreover, a single covariance matrix is estimated per eachsub-band (and also inverse Cholesky of the covariance matrix), thisreduces complexity and also necessary for good covariance estimation. Inthe calculation of each sub-band's covariance matrix, the N sub-carriersof the sub-band are used and up to Ñ subcarriers from the adjacentsub-bands. Here, for simplicity we assume Ñ=0.

Table I below summarizes the different variables (vectors, matrices,etc.) used by the D-IRC algorithm per the kth SC, and gives theredimensions and description.

TABLE I Description of different variables of D-IRC algorithm and theirdimensions Variable Dimensions Description y_(k) N_(r) × 1 Receivedinput vector for the kth SC H_(k) N_(r) × N_(s) Estimated channel forthe kth SC C_(k) N_(r) × N_(r) Estimated noise covariance matrix of thekth SC (same covariance for a sub-band of N SC) G _(k) N_(s) × N_(r)LMMSE equalizer for the kth SC. C_(k) ^(−1/2) N_(r) × N_(r) InverseCholesky of the covariance matrix C_(k) (same inverse Cholesky for aband of N SC) {circumflex over (σ)}_(k) ² N_(s) × 1 Post processingnoise variance (1/ppSNR) of the kth SC {circumflex over (x)}_(k) N_(s) ×1 Estimated TX vector for the kth SC (at the LMMSE equalizer output){circumflex over (x)}_(k) = G _(k)C_(k) ^(−1/2)y _(k) x _(k) N_(s) × 1Soft symbols for the kth SC σ _(k) ² N_(s) × 1 Soft slicing variance forthe kth SC Δ_(k) N_(s) × N_(s) Diagonal matrix whose diagonal holds theaverage of σ _(k) ² over the whole sub-band (same matrix for a sub-bandof N SC) {tilde over (H)} _(k) N_(r) × N_(s) Whitened channel for thekth SC, {tilde over (H)}_(k) = C_(k) ^(−1/2)H_(k) G _(prev, k) N_(s) ×N_(r) The equalizer calculated at previous iteration if iterationindex > o, and in previous symbol if iteration index = o C _(prev, k)^(−1/2) N_(r) × N_(r) Inverse Cholesky calculated at previous iterationif iteration index > o, and in previous symbol if iteration index = o{circumflex over (σ)}_(prev, k) ² N_(s) × 1 The post processing noisevariance vector calculated at previous iteration if iteration index > o,and in previous symbol if iteration index = o

The inputs of the D-IRC algorithm may be, for each OFDM symbol y_(k) ofthat symbol and H_(k) (which may be calculated at the preamble). Inaddition, at the first symbol after preamble, the G_(prev), C_(prev)^(−1/2) and {circumflex over (σ)}_(prev) ² are initialized to G,C^(−1/2), and {circumflex over (σ)}² from the preamble. The outputs ofthe D-IRC algorithm are G, C^(−1/2) and {circumflex over (σ)}² (wherelinear decoder is assumed for now).

In the diagram 600 depicting the D-IRC algorithm, the SC index k isomitted for the sake of simplicity.

The D-IRC block diagram 600 may comprise several sub-blocks includingthe equalization sub-block 601, the soft-slicing sub-block 602, thecovariance estimation sub-block 603, the inverse Cholesky sub-block 604,the H whitening sub-block 605, the Slicing var calculation sub-block606, and the Equalizer and pp SNR calculation sub-block 607.

Moreover, the D-IRC algorithm may perform a predefined number of Literations and an additional equalization step each OFDM symbol. Eachiteration comprises performing equalization, soft-slicing, covarianceestimation, inverse Cholesky, H whitening, Slicing var calculation, andEqualizer and pp SNR calculation steps. At the first iteration, theequalizer, inverse Cholesky matrix and post processing noise varianceare taken from previous symbol. Otherwise, they are taken from previousiteration.

Let us elaborate on the sub-blocks of the D-IRC algorithm.

For instance, the D-IRC algorithm may comprises several sub-blocks inwhich in each sub-blocks a respective operation is performed.

The equalization sub-block 601 may whiten the y_(k) and then applies theequalization matrix G_(k), according to Eq. (10) as follows:{circumflex over (x)} _(k) =G _(k) C _(prev,k) ^(−1/2) y _(k).  Eq. (10)

In some embodiments of this disclosure (for example, the multi-streamcase), it seems better to first whiten y_(k), i.e., according to Eq.(11) as follows:{tilde over (y)} _(k) =C _(prev,k) ^(−1/2) y _(k)  Eq. (11)

and then equalize by G_(k).

In some embodiments of this disclosure (for example, the single streamcase), it may be sometimes better to first calculate, i.e., according toEq. (12) as follows:w ^(H) =G _(k) C _(prev,k) ^(−1/2)  Eq. (12)and then apply{circumflex over (x)} _(k) =w ^(H) y _(k).  Eq. (13)

The soft-slicing sub-block 602 applies on the r^(th) stream the softslicing function as follows:

$\begin{matrix}{{\left\lbrack \overset{\_}{x} \right\rbrack_{r} = {\lbrack\lambda\rbrack_{r} + {\tanh\left( \frac{d_{\min}{{Re}\left( \left\lbrack {\hat{x} - \lambda} \right\rbrack_{r} \right.}}{\left\lbrack {\hat{\sigma}}^{2} \right\rbrack_{r}} \right)} + {j\;{\tanh\left( \frac{d_{\min}{{Im}\left( \left\lbrack {\hat{x} - \lambda} \right\rbrack_{r} \right.}}{\left\lbrack {\hat{\sigma}}^{2} \right\rbrack_{r}} \right)}}}},\mspace{20mu}{where}} & {{Eq}.\mspace{11mu}(14)} \\{\lambda = {{\frac{d_{\min}}{2}{\min\left( {{\max\left( {{{2\left\lfloor {\frac{{Re}\left( \hat{x} \right)}{d_{\min}} - \frac{1}{2}} \right\rfloor} + 2},{{- \sqrt{M}} + 2}} \right)},{\sqrt{M} - 2}} \right)}} + {j\frac{d_{\min}}{2}{\min\left( {{\max\left( {{{2\left\lfloor {\frac{{Im}\left( \hat{x} \right)}{d_{\min}} - \frac{1}{2}} \right\rfloor} + 2},{{- \sqrt{M}} + 2}} \right)},{\sqrt{M} - 2}} \right)}}}} & {{Eq}.\mspace{11mu}(15)}\end{matrix}$and all the operations above on the vector {circumflex over (x)} may beunderstood as element-wise.

Note that in constellations higher than QPSK, the soft slicing is anapproximation of the full conditional mean. When treating the real andimaginary components of [x]_(r) as PAM constellations, only the 2closest points are considered and the remaining points ignored.Simulation results show that this approximation does not have anynoticeable effect on performance.

The soft slicing noise variance σ ² is approximated as:

$\begin{matrix}{\left\lbrack {\overset{\_}{\sigma}}^{2} \right\rbrack_{r} = {\frac{d_{\min}^{2}}{2} = {{\left\lbrack {\lambda - \overset{\_}{x}} \right\rbrack_{r}}^{2}.}}} & {{Eq}.\mspace{11mu}(16)}\end{matrix}$To see that, note that in the case of QPSK:[σ ²]_(r) =E([{circumflex over (x)}−x] _(r)|² |y)=E(|[{circumflex over(x)}] _(r)|² |y)−2 Re(E([{circumflex over (x)}] _(r) [x] _(r)*))+|[ x]_(r)|²=1−|[ x] _(r)|².  Eq. (17)

In the case of higher modulations, the same approximation of consideringonly the two closest points in each component of [x]_(r) yields thedesired result.

The D-IRC block diagram may further comprise the covariance estimationsub-block 603. Here, the SCs are divided into sub-bands and a singlecovariance is estimated per band.

For example, when fixing a sub-band and when k denote a SC in thesub-band, then the covariance is estimated according to:

$\begin{matrix}{{C_{k} = {{\frac{1}{N}{\sum\limits_{i}{\left( {y_{i} - {H_{i}{\overset{¯}{x}}_{i}}} \right)\left( {y_{i} - {H_{i}{\overset{¯}{x}}_{i}}} \right)^{H}}}} + {\beta I}}},} & {{Eq}.\mspace{11mu}(18)}\end{matrix}$where the summation is over all N SC in the sub-band. The β parameter isneeded to account for some necessary diagonal loading.

A more accurate estimation for noise covariance may be:

$\begin{matrix}{{{\overset{˜}{C}}_{k} = {{\frac{1}{N}{\sum\limits_{i}{\left( {y_{i} - {H_{i}{\overset{¯}{x}}_{i}}} \right)\left( {y_{i} - {H_{i}{\overset{¯}{x}}_{i}}} \right)^{H}}}} + {\beta I} + {H_{k}\Delta_{k}H_{k}^{H}}}}.} & {{Eq}.\mspace{11mu}(19)}\end{matrix}$

So in fact when C_(k) is used instead of the more accurate {tilde over(C)}_(k), the resulted noise covariance matrix is not the identitymatrix.

On the other hand, when using the more accurate version {tilde over(C)}_(k), results in losing the property that the estimated covarianceis fixed for the whole sub-band and significantly increases thecomplexity. This problem may be tackled by using the less accurate C_(k)and compensating for the H_(k)Δ_(k)H_(k) ^(H) term in the equalizer andpost processing noise variance calculations.

The Inverse Cholesky sub-block 604 performs Cholesky decomposition andthen inversion of the resulted lower triangular matrix; it outputs thematrix C_(k) ^(−1/2).

The H whitening sub-block 605 simply calculates Eq. (20) as follows:{tilde over (H)} _(k) =C _(k) ^(−1/2) H _(k).  Eq. (20)

The Slicing var estimation sub-block 606 estimates the variance of thesoft symbols per stream and outputs it as the diagonal of the diagonalmatrix Δ_(k). This is done by averaging over all SCs in the band, i.e.,according to Eq. (21) as follows:

$\begin{matrix}{{\Delta_{k} = {{diag}\left( {\frac{1}{N}{\sum\limits_{i}{\overset{¯}{\sigma}}_{i}^{2}}} \right)}},} & {{Eq}.\mspace{11mu}(21)}\end{matrix}$where the summation is over all subcarriers in the band.

The D-IRC block diagram may further comprise the equalizer and PP SINRcalculation sub-block 607. For example, First, the H_(k)Δ_(k)H_(k) ^(H)term mentioned earlier may be ignored Then in the multi-stream case, thefollowing equations may be derived:{circumflex over (σ)}_(k) ²′=diag(γ_(k)′)⁻¹ diag(R _(k)′)  Eq. (22)G _(k)′=diag(γ_(k)′)⁻¹ R _(k) ′{tilde over (H)} _(k) ^(H),  Eq. (23)whereR _(k)′=({tilde over (H)} _(k) ^(H) {tilde over (H)} _(k) +I)⁻¹  Eq.(24)andγ_(k)′=1−diag(R _(k)).  Eq. (25)

This is reduced in the single stream case to:

$\begin{matrix}{{\hat{\sigma}}_{k}^{2\prime} = \frac{1}{{{\overset{\sim}{H}}_{k}}^{2}}} & {{Eq}.\mspace{11mu}(26)} \\{G_{k}^{\prime} = {\frac{{\overset{\sim}{H}}_{k}^{H}}{{{\overset{\sim}{H}}_{k}}^{2}}.}} & {{Eq}.\mspace{11mu}(27)}\end{matrix}$

Next, by taking into account the H_(k)Δ_(k)H_(k) ^(H) term. It can beshown that:{circumflex over (σ)}_(k) ²=diag(γ_(k))⁻¹diag(R _(k))+diag(Δ)  Eq. (28)G _(k)=diag(γ_(k))⁻¹ R _(k) {tilde over (H)} _(k) ^(H),  Eq. (29)whereR _(k)=({tilde over (H)} ^(H) {tilde over (H)}+(I+Δ)⁻¹)⁻¹ andγ_(k)=1−(I+Δ)⁻¹diag(R _(k)).  Eq. (30)In the single stream case this reduces into:

$\begin{matrix}{{\hat{\sigma}}_{k}^{2} = {\frac{1}{{{\overset{\sim}{H}}_{k}}^{2}} + \Delta}} & {{Eq}.\mspace{11mu}(31)} \\{G_{k} = {\frac{{\overset{\sim}{H}}_{k}^{H}}{{{\overset{\sim}{H}}_{k}}^{2}}.}} & {{Eq}.\mspace{11mu}(32)}\end{matrix}$Note: in the single stream case the Δ correction only effects{circumflex over (σ)}_(k) ² and in fact {circumflex over (σ)}_(k)²={circumflex over (σ)}_(k) ²′+Δ and G_(k)=G_(k)′. In the multi-stream,this is not true anymore, but in fact holds approximately, in the sensethat the whole Δ correction mainly effects the calculation of{circumflex over (σ)}_(k) ². Indeed, if assuming [Δ]_(r,r)<1 for r=1, .. . , N_(s) then it may be possible to approximate I+Δ≈I and it may bepossible to get R_(k)≈R_(k)′ and γ_(k)≈γ_(k)′. So in fact,{circumflex over (σ)}_(k) ²≈{circumflex over (σ)}_(k) ²′+diag(Δ) and G_(k) ≈G _(k)′.  Eq. (33)

This can enable to work with standard MMSE equalizer and not make thenecessary modifications. Note however, that the Δ term is significant inpost-processing noise variance calculations and without it, thecalculated post-processing noise variance {circumflex over (σ)}_(k) ²can be significantly underestimated.

Reference is made to FIG. 7 which is a schematic view of a block diagram700 illustrating a D-IRC SNR detector processing until the SINR drop isdetected.

The D-IRC SNR detector may determine whether to enable the D-IRC or not;this is important in order to avoid degradation in the case ofno-interference and the case of static interference relative to non-IRCdecoding and IRC, respectively.

The D-IRC SNR detector can make decisions separately on 20 Mhz bands,i.e., it may employ D-IRC on 20 Mhz bands and non-IRC decoding onanother band (other granularities are also possible). In the following,is focused on a single 20 Mhz band.

Initially the processing starts from non-IRC or plain vanilla IRC, wherenon-IRC is the default choice and IRC is the choice in case that it isdetermined there is an interference on the preamble. Each OFDM symbol adetector is used to detect a drop in post-processing SINR level.Moreover, only if such a drop is detected the processing switches fromnon-IRC/IRC to the D-IRC and performs the D-IRC throughout the remainingsymbols. Thus in the case of no interference or static interference, theperformance should remain similar to that of non-IRC and IRC,respectively.

Here, the processing is described up to the OFDM symbol in which dynamicinterference is detected (afterwards the algorithm becomes D-IRC). Thealgorithm is based on part of the calculations done in the firstiteration of D-IRC (mainly the soft-slicing) and a detector that detectspost-processing SINR drop. If at a certain symbol the detector detects adrop, it outputs D-IRC enable=1; the D-IRC completes the first iterationand continues with remaining iterations, and in the remaining symbols itworks as plan D-IRC.

In the diagram 700, the G_(prev) is the equalizer calculated on thepreamble and it is not recalculated, unless SINR drop is detected. AlsoC_(prev) ^(−1/2) is based on the preamble. It should be understood asthe inverse Cholesky of the covariance matrix estimated on the preamble,in the case of starting from IRC on the preamble. Moreover, in the caseof non-IRC, it should be understood as a diagonal matrix whose i^(th)diagonal element holds

$\frac{1}{\sigma_{i}},$where σ_(i) is the estimated noise variance of the i^(th) RX antenna.Finally, {circumflex over (σ)}_(prev) ² is also taken from the preambleand is the post-processing noise variance vector and is also averaged(once during preamble) over all SCs in the 20 MHz band to yield theλ_(prev).

The D-IRC sub-blocks that are employed each symbol are the equalization701, which yields {circumflex over (x)}, the soft-slicing 702, whichyields x and σ ², and the slicing var estimation 703, which yields Δ.The above mentioned sub-blocks may perform similar operations as thecorresponding sub-blocks of the D-IRC algorithm 600 of FIG. 6 .Moreover, the new sub-blocks are the post-processing noise varestimation 704 and post processing SINR drop detector 705 describedbelow.

The post-processing noise variance estimation sub-block 704 works oneach sub-band. The inputs of the post-processing noise varianceestimation sub-block 704 are x and σ ² for all SCs in the given sub-bandand Δ per sub-band.

The output of the post-processing noise variance estimation sub-block704 is:

$\begin{matrix}{{{\hat{\sigma}}^{2} = {{\frac{1}{N}{\sum\limits_{i}{{{\overset{\_}{x}}_{i} - {\hat{x}}_{i}}}^{2}}} + \Delta}},} & {{Eq}.\mspace{11mu}(34)}\end{matrix}$where the sum is over all SCs in the sub-band.

The Post-processing SINR drop detector sub-block 705 averages{circumflex over (σ)}² over all sub-bands to yield λ and compares it toλ_(prev) If max (λ−λ_(prev))≥SINR_DROP_TH, then D-IRC enable=1,otherwise it equals 0.

FIG. 8 shows a method 800 according to an embodiment of this disclosurefor dynamically reducing interference for a multi-carrier communicationreceiver. The method 800 may be carried out by the device 100, as itdescribed above.

The method 800 comprises a step 801 of calculating a set of channelestimates 101 for a plurality of sub-carriers no based on a plurality ofpilot-symbols and/or data-symbols carried by said plurality ofsub-carriers 110.

The method 800 further comprises a step 802 of calculating a set ofequalizers 102 for the plurality of sub-carriers no based on the set ofchannel estimates 101.

The method 800 further comprises a step 803 of performing anequalization on a plurality of data-symbols using the set of equalizers102 for obtaining a plurality of equalized symbols 103.

The method 800 further comprises a step 804 of performing a soft-slicingor hard-slicing procedure comprising obtaining a plurality of estimatedsymbols 104 based on the equalized symbols 103.

The method 800 further comprises a step 805 of calculating at least onediagonal-loaded interference-plus-noise covariance matrix 105, based onthe set of estimated symbols 104.

The method 800 further comprises a step 806 of de-mapping the pluralityof data symbols to soft-bits 106, based on the at least onediagonal-loaded interference-plus-noise covariance matrix 105, the setof channel estimates 101 or an updated version thereof.

The method 800 further comprises a step 807 of feeding the soft-bits 106to a channel decoder.

This disclosure has been described in conjunction with variousembodiments as examples as well as implementations. However, othervariations can be understood and effected by those persons skilled inthe art and practicing the claimed invention, from the studies of thedrawings, this disclosure and the independent claims. In the claims aswell as in the description the word “comprising” does not exclude otherelements or steps and the indefinite article “a” or “an” does notexclude a plurality. A single element or other unit may fulfill thefunctions of several entities or items recited in the claims. The merefact that certain measures are recited in the mutual different dependentclaims does not indicate that a combination of these measures cannot beused in an advantageous implementation.

What is claimed is:
 1. A device, comprising: at least one processor; anda non-transitory computer-readable storage medium storing a program thatis executable by the at least one processor, the program comprisinginstructions to: calculate a set of channel estimates for a plurality ofsub-carriers based on a plurality of pilot symbols or a first pluralityof data symbols carried by the plurality of sub-carriers; calculate aset of equalizers for the plurality of sub-carriers based on the set ofchannel estimates; perform an equalization on a second plurality of datasymbols, using the set of equalizers, to obtain a plurality of equalizedsymbols, wherein the first plurality of data symbols and the secondplurality of data symbols are a same set of data symbols or a differentset of data symbols; perform a soft-slicing procedure or a hard-slicingprocedure comprising obtaining a plurality of estimated symbols based onthe plurality of equalized symbols; calculate at least onediagonal-loaded interference-plus-noise covariance matrix, based on theplurality of estimated symbols; de-map the second plurality of datasymbols to soft-bits, based on the at least one diagonal-loadedinterference-plus-noise covariance matrix, the set of channel estimates,or an updated version of the set of channel estimates; and feed thesoft-bits to a channel decoder.
 2. The device according to claim 1,wherein the program further comprises instructions to: calculate asubsequent set of equalizers for the plurality of sub-carriers, based onthe at least one diagonal-loaded interference-plus-noise covariancematrix and the set of channel estimates or the updated version of theset of channel estimates.
 3. The device according to claim 2, whereinthe program further comprises instructions to: calculate at least onewhitening matrix based on the at least one diagonal-loadedinterference-plus-noise covariance matrix; and wherein calculating thesubsequent set of equalizers for the plurality of sub-carriers, based onthe at least one diagonal-loaded interference-plus-noise covariancematrix and the set of channel estimates or the updated version of theset of channel estimates, comprises: calculating the subsequent set ofequalizers for the plurality of sub-carriers based on the at least onewhitening matrix, the at least one diagonal-loadedinterference-plus-noise covariance matrix, and the set of channelestimates or the updated version of the set of channel estimates; andwherein de-mapping the second plurality of data symbols to soft-bits,based on the at least one diagonal-loaded interference-plus-noisecovariance matrix, the set of channel estimates, or the updated versionof the set of channel estimates comprises: de-mapping the secondplurality of data symbols into soft-bits, based on the at least onewhitening matrix and the at least one diagonal-loadedinterference-plus-noise covariance matrix, the set of channel estimates,or the updated version of the set of channel estimates.
 4. The deviceaccording to claim 3, wherein the program further comprises instructionsto: perform a whitening operation on the second plurality of datasymbols, using the at least one whitening matrix, to obtain a pluralityof whitened data symbols; and perform a whitening operation on the setof channel estimates or the updated version of the set of channelestimates, using the at least one whitening matrix, to obtain a set ofwhitened channel estimates; wherein calculating the subsequent set ofequalizers for the plurality of sub-carriers based on the at least onewhitening matrix, the at least one diagonal-loadedinterference-plus-noise covariance matrix, and the set of channelestimates or the updated version of the set of channel estimatescomprises: calculating the subsequent set of equalizers for theplurality of sub-carriers based on calculating the subsequent set ofequalizers for the plurality of sub-carriers based on the set ofwhitened channel estimates, the at least one whitening matrix, and theat least one diagonal-loaded interference-plus-noise covariance matrix;and wherein de-mapping the second plurality of data symbols intosoft-bits, based on the at least one whitening matrix and the at leastone diagonal-loaded interference-plus-noise covariance matrix, and theset of channel estimates, or the updated version of the set of channelestimates, comprises: de-mapping the whitened data symbols intosoft-bits, based on the subsequent set of equalizers or the whitenedchannel estimates, the at least one whitening matrix, and the at leastone diagonal-loaded interference-plus-noise covariance matrix.
 5. Thedevice according to claim 4, wherein the program comprises instructionsto: whiten a received input vector for a determined sub-carrier (SC)based on an inverse Cholesky matrix of the at least one diagonal-loadedinterference-plus-noise covariance matrix, and apply the subsequent setof equalizers on the whitened input vector for the determined SC; orapply the subsequent set of equalizers on an inverse Cholesky matrix ofthe at least one diagonal-loaded interference-plus-noise covariancematrix, to obtain a result, and apply the obtained result on a receivedinput vector for a determined sub-carrier (SC).
 6. The device accordingto claim 4, wherein the program comprises further instructions to:perform an inner-iteration procedure comprising performing a predefinednumber of inner-iterations, wherein each inner-iteration is performed onthe same plurality of data symbols, and wherein in each inner-iteration,the following steps are performed: performing the equalization using theset of equalizers calculated in a previous inner-iteration, or in a caseof a first inner-iteration, using the set of equalizers calculated forthe plurality of sub-carriers based on the set of channel estimates,performing the soft-slicing procedure or the hard-slicing procedure,calculating the at least one diagonal-loaded interference-plus-noisecovariance matrix, calculating the at least one whitening matrix,performing the whitening operation on the second plurality of datasymbols, performing the whitening operation to obtain the set ofwhitened channel estimates, and calculating another set of equalizers.7. The device according to claim 4, wherein the program comprisesinstructions to: perform an outer-iteration procedure comprisingperforming a predefined number of outer-iterations, wherein eachouter-iteration is performed on a different plurality of data-symbolsthan a previous outer-iteration, and wherein in each outer-iteration thefollowing steps are performed: performing the equalization using the setof equalizers calculated in a previous outer-iteration or aninner-iteration, or in a case of a first outer-iteration, using the setof equalizers calculated for the plurality of sub-carriers based on theset of channel estimates, performing the soft-slicing procedure or thehard-slicing procedure, calculating the at least one diagonal-loadedinterference-plus-noise covariance matrix, calculating the at least onewhitening matrix, performing the whitening operation on the secondplurality of data symbols, performing the whitening operation to obtainthe set of whitened channel estimates, and calculating another set ofequalizers.
 8. The device according to claim 7, wherein a predefinednumber of outer-iterations is determined based on a detected drop inper-stream post processing Signal-to-Interference-Plus-Noise Ratio(pp-SINR) level.
 9. The device according to claim 4, wherein performingthe whitening operation on the set of channel estimates or the updatedversion of the set of channel estimates, using the at least onewhitening matrix, to obtain the set of whitened channel estimates,comprises: multiplying at least one channel estimates matrix for adetermined sub-carrier (SC), or an updated version thereof, by aninverse Cholesky matrix of the at least one diagonal-loadedinterference-plus-noise covariance matrix.
 10. The device according toclaim 1, wherein calculating the at least one diagonal-loadedinterference-plus-noise covariance matrix, based on the plurality ofestimated symbols comprises: calculating at least oneinterference-plus-noise covariance matrix; and adding a diagonal matrixto the at least one interference-plus-noise covariance matrix, tocalculate the at least one diagonal-loaded interference-plus-noisecovariance matrix.
 11. The device according to claim 1, wherein theprogram further comprises instructions to: calculate an inverse Choleskymatrix of the at least one diagonal-loaded interference-plus-noisecovariance matrix.
 12. The device according to claim 11, wherein theinverse Cholesky matrix is calculated by performing a Choleskydecomposition on the at least one diagonal-loadedinterference-plus-noise covariance matrix.
 13. The device according toclaim 1, wherein the program further includes instructions to: divide aplurality of sub-carriers (SCs) into sub-bands, wherein each sub-bandcomprises a predetermined number of SCs, and wherein aninterference-plus-noise covariance matrix is estimated per eachsub-band.
 14. The device according to claim 1, wherein the programfurther includes instructions to: calculate a first set of per-streampost processing Signal-to-Interference-Plus-Noise Ratio (pp-SINR) forthe plurality of sub-carriers based on the plurality of pilot symbols,or the first plurality of data symbols or the set of channel estimatescarried by the plurality of sub-carriers; and calculate a second set ofper stream pp-SINR for the plurality of sub-carriers based on aplurality of equalized symbols and a plurality of estimated symbols. 15.The device according to claim 14, wherein the program further includesinstructions to: calculate, from the first set of per-stream pp-SINR, asingle per-stream pp-SINR vector representing an average pp-SINR of thefirst set of per-stream pp-SINR; calculate, from the second set ofper-stream pp-SINR, a single per-stream pp-SINR vector representing anaverage pp-SINR of the second set of per-stream pp-SINR; and calculate app-SINR difference vector as the difference between the average pp-SINRof the first set of per-stream pp-SINR and the average pp-SINR of thesecond set of per-stream pp-SINR.
 16. The device according to claim 15,wherein the program further includes instructions to: detect a drop inpp-SINR level for the plurality of sub-carriers.
 17. The deviceaccording to claim 16, wherein the drop in the pp-SINR is detected basedon the pp-SINR difference vector.
 18. The device according to claim 1,wherein the set of equalizers is based on a Minimum Mean Square Error(MMSE) equalizer or a Zero Forcing (ZF) equalizer.
 19. A method,comprising: calculating a set of channel estimates for a plurality ofsub-carriers based on a plurality of pilot symbols or a first pluralityof data symbols carried by the plurality of sub-carriers; calculating aset of equalizers for the plurality of sub-carriers based on the set ofchannel estimates; performing an equalization on a second plurality ofdata symbols using the set of equalizers, to obtain a plurality ofequalized symbols, wherein the first plurality of data symbols and thesecond plurality of data symbols are a same set of data symbols or adifferent set of data symbols; performing a soft-slicing procedure or ahard-slicing procedure comprising obtaining a plurality of estimatedsymbols based on the plurality of equalized symbols; calculating atleast one diagonal-loaded interference-plus-noise covariance matrix,based on the plurality of estimated symbols; de-mapping the secondplurality of data symbols to soft-bits, based on the at least onediagonal-loaded interference-plus-noise covariance matrix, the set ofchannel estimates, or an updated version of the set of channelestimates; and feeding the soft-bits to a channel decoder.
 20. Anon-transitory computer-readable storage medium comprising codes thatare executable by a computer, wherein the codes comprise instructionsfor: calculating a set of channel estimates for a plurality ofsub-carriers based on a plurality of pilot symbols or a first pluralityof data symbols carried by the plurality of sub-carriers; calculating aset of equalizers for the plurality of sub-carriers based on the set ofchannel estimates; performing an equalization on a second plurality ofdata symbols using the set of equalizers, to obtain a plurality ofequalized symbols, wherein the first plurality of data symbols and thesecond plurality of data symbols are a same set of data symbols or adifferent set of data symbols; performing a soft-slicing procedure or ahard-slicing procedure comprising obtaining a plurality of estimatedsymbols based on the plurality of equalized symbols; calculating atleast one diagonal-loaded interference-plus-noise covariance matrix,based on the plurality of estimated symbols; de-mapping the secondplurality of data symbols to soft-bits, based on the at least onediagonal-loaded interference-plus-noise covariance matrix, the set ofchannel estimates, or an updated version of the set of channelestimates; and feeding the soft-bits to a channel decoder.